Load Data

For details about data description see here

load(file = "../output/mediatenor.Rda")

Point Plots

Tageszeitungen

p <- df.reduced %>%
  filter(category == "daily_print") %>%
  filter(medium != "Berliner") %>%
  ggplot(aes(year, wertung, color=p_group, group=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Tageszeitungen (ungewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 23 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "wertung"))
p <- df.reduced %>%
  filter(category == "daily_print") %>%
  filter(medium != "Berliner") %>%
  ggplot(aes(year, weighted, color=p_group, group=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Tageszeitungen (gewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 23 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "weighted"))

Magazine und Wochenzeitungen

p <- df.reduced %>%
  filter(category == "magazine_print") %>%
  ggplot(aes(year, wertung, color=p_group, group = p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 5) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Magazine und Wochenzeitungen (ungewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 13 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "wertung"))
p <- df.reduced %>%
  filter(category == "magazine_print") %>%
  ggplot(aes(year, weighted, color=p_group, group = p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 5) +
    geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Magazine und Wochenzeitungen (gewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 13 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "weighted"))

Nachrichtensendungen

p <- df.reduced %>%
  filter(category == "news_tv") %>%
  ggplot(aes(year, wertung, color=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Nachrichtensendungen (ungewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 4 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "wertung"))
p <- df.reduced %>%
  filter(category == "news_tv") %>%
  ggplot(aes(year, weighted, color=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 3) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Nachrichtensendungen (gewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p
## Warning: Removed 4 rows containing missing values (geom_point).

#ggplotly(p, tooltop=c("medium", "weighted"))

Politische TV-Shows

p <- df.reduced %>%
  filter(category == "polit_tv") %>%
  ggplot(aes(year, wertung, color=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 6) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Politische TV-Shows (ungewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p

#ggplotly(p, tooltop=c("medium", "wertung"))
p <- df.reduced %>%
  filter(category == "polit_tv") %>%
  ggplot(aes(year, weighted, color=p_group)) +
  geom_point(size=0.8) + geom_line() +
  facet_wrap(~medium, ncol = 6) +
  geom_hline(yintercept = 0, color="grey10", 
             size=0.3, linetype = 2) +
  labs(x="", y="", title="Politische TV-Shows (gewichtet)", color="") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2)) 

p

#ggplotly(p, tooltop=c("medium", "weighted"))

Radarcharts

require(ggiraph)
require(ggiraphExtra)

Tageszeitungen

radar <- df.reduced %>% 
  filter(category == "daily_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = wertung)
radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Tageszeitungen (ungewichtet)")

radar <- df.reduced %>% 
  filter(category == "daily_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Tageszeitungen (gewichtet)")

Magazine und Wochenzeitungen

radar <- df.reduced %>% 
  filter(category == "magazine_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = wertung)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Magazine und Wochenzeitungen (ungewichtet)")

radar <- df.reduced %>% 
  filter(category == "magazine_print") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Magazine und Wochenzeitungen (gewichtet)")

Nachritensendungen

radar <- df.reduced %>% 
  filter(category == "news_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = wertung)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Nachritensendungen (ungewichtet)")

radar <- df.reduced %>% 
  filter(category == "news_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Nachritensendungen (gewichtet)")

Politische TV-Shows

radar <- df.reduced %>% 
  filter(category == "polit_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = wertung)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Politische TV-Shows (ungewichtet)")

radar <- df.reduced %>% 
  filter(category == "polit_tv") %>%
  group_by(medium, p_group) %>%
  dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
  ungroup() %>%
  spread(key=p_group, value = weighted)

radar %>%
  ggRadar(aes(color=medium), rescale = F,
          alpha = 0, legend.position = "right") +
  labs(title = "Politische TV-Shows (gewichtet)")